The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Four-dimensional (4-D) medical images containing information of 3-D volumes moving in time are also known. Such 2-D, 3-D or 4-D images are processed using medical image recognition techniques to determine the presence of anatomical structures (e.g., lung, heart, head, chest, etc.) or abnormalities (e.g., lesions, cysts, tumors, polyps, etc).
Traditionally, Computer-Aided Detection (CAD) refers to automatic image processing and recognition of abnormal (or diseased) tissues or structures within a medical image. However, in a broader sense and as used herein, “CAD” can also be used to refer to the detection of both abnormal and normal structures. In this sense, it becomes more related to the general research topic of computer vision and image understanding. The CAD system may process medical images, identify and/or tag anatomical structures or possible abnormalities for further processing or review. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out, or align to, anatomical features (e.g., pelvis, heart, liver, knee meniscus, etc.) in the selected regions of an image to a doctor for easier visualization and accelerated diagnosis of any disease or condition.
There have been significant advances in the research field of general-purpose object detection and pattern recognition in images. However, image understanding algorithms that work well with natural images may not work robustly enough with medical images. This is mainly because medical images exhibit strong variability, where anomaly is a norm. The strong variability in medical images may be the result of for example, severe diseases, fatty tissue, deformity, implants, imaging artifacts, occlusions, missing data, abnormal field-of-view during image acquisition, and so forth. Conventional methods are unable to provide the level of robustness in the presence of such strong variability in the images.
Another challenge involves the particularly stringent requirements for robustness and accuracy in clinical use applications. For example, in radiological imaging, it is desirable to minimize the time and area of exposure of the patient to potentially harmful radiation. A bigger-than-necessary scanning field may result in more harm to the patient due to exposure to added radiation, and images with lower voxel resolution. On the other hand, if the scanning field is smaller-than-necessary, there is a risk of missing pertinent structural data, which may prompt the need for a re-scan, thereby resulting in waste of time and potentially more exposure to radiation. It is therefore important to accurately and robustly identify the scanning field so that the resulting images capture the desired target structure. The more precise the scanning field, the faster the data acquisition can be performed and the lesser the subject is exposed to potentially harmful radiation.
Accordingly, it would be desirable to provide improved systems and methods to facilitate robust computer-aided detection and image understanding.